eupskills | Introduction to Machine Learning

Course / Course Details

Introduction to Machine Learning

Data Science & Analytics

By - Super admin
0 students
6 Hours
(0)

Course Requirements

Basic Programming Knowledge (preferably Python)

Understanding of Linear Algebra and Calculus

Familiarity with Probability and Statistics

A laptop with internet access

Optional: Prior experience with libraries like NumPy, Pandas, or Scikit-learn is a plus

Course Description

This course provides a comprehensive introduction to Machine Learning (ML). Students will learn the fundamental concepts of supervised and unsupervised learning, model evaluation, and deployment. The course emphasizes hands-on implementation using Python and popular ML libraries. Real-world datasets and case studies are included to build practical understanding.

Topics include:

Regression, Classification, Clustering
Decision Trees, Random Forests, SVM, k-NN
Neural Networks and Deep Learning (Intro)
Model tuning, cross-validation
Real-world projects and deployment

Course Outcomes

By the end of this course, learners will be able to:

Understand key machine learning concepts and algorithms
Build, train, and evaluate machine learning models using Python
Apply ML techniques to solve real-world problems
Analyze datasets and extract meaningful insights
Deploy basic ML models using cloud or local environments
Communicate model performance using standard metrics and visuals

Course Curriculum

2 chapters
2 lectures
0 quizzes
6 Hours total length

Toggle all chapters

1 What is Machine Learning?

1 Hour

Overview: Learn what Machine Learning is, how it differs from traditional programming, and where it’s used in real life. Key Topics: Definition and types of Machine Learning (Supervised, Unsupervised, Reinforcement) Real-world applications (e.g., recommendation systems, fraud detection) Machine Learning vs Artificial Intelligence vs Deep Learning

1 Linear Regression

1 Hour

Overview: Understand how to model relationships between variables using linear regression and make predictions. Key Topics: Concept of dependent and independent variables Least Squares Method Evaluation metrics: MSE, R² score Hands-on implementation using Python (scikit-learn)

1. Presentation

Instructor

Super admin

As the Super Admin of our platform, I bring over a decade of experience in managing and leading digital transformation initiatives. My journey began in the tech industry as a developer, and I have since evolved into a strategic leader with a focus on innovation and operational excellence. I am passionate about leveraging technology to solve complex problems and drive organizational growth. Outside of work, I enjoy mentoring aspiring tech professionals and staying updated with the latest industry trends.

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